glossary.yaml

---
# In this project I use several names interchangingly.
# Here is the catalog of these names.
common:
  Columns:
    description: static class that contains constant coding-friendly (latin alphanumerics, no whitespaces, etc) field names in order to avoid hardcoding them when creating `dict` or `pd.Series` or `pd.DataFrame` objects. I never create instances of this class. May contain static methods that parse data or aid somehow (for example validate something). Have reserved variable `Columns.texts`, which is a dictionary with screen names for all necessary field names
    formats: # how it may be coded
      class ColumnsSomethins: python static class, may inherit from something
      c: short name of Columns
    all alternative names: # how it may be described
      - Columns
      - BaseColumns (when inheriting)
      - ColunmsSomething (in code word `Columns` should be the first one, in filename.py word `Columns` should be the last one)
  Presets:
    description: python class that allows me to easily manage the contents of a single predefined `yaml` file. Has it's own `Columns`. Relies on `Columns` to parse data.
    formats: # how it may be coded
      class PresetSomething: python class, inherits from common.PresetsManager
      p, preset: instance if PresetsSomething
    all alternative names: # how it may be described
      - Columns
      - BaseColumns (when inheriting)
      - ColunmsSomething (in code word `Columns` should be the first one, in filename.py word `Columns` should be the last one)
neo4j:
  conn:
    description: python class that can communicate with server
    formats: # how it may be coded
      Connection: python class
      conn: instance of Connection
    all alternative names: # how it may be described
      - Connection
      - conn
  db:
    description: some `database` on some `neo4j` server. There may be many of them.
    formats: # how it may be coded
      raw: remote data on server
      db_name: string with specific name
      response: specific return type from conn.query
    all alternative names: # how it may be described
      - db
      - database
      - db_name
  query:
    description: a command that i send to neo4j server
    formats: # how it may be coded
      string: either hardcoded or formatted
    all alternative names: # how it may be described
      - query
      - command
      - rule
  node:
    description: an entry that exists in some `db`. I access it with queries to server. Can be converted into `row`.
    formats: # how it may be coded
      raw: remote data on server
      string: formatting string like f'({NODE}:{Label})', to be used in `query`
      part of `query` string: MATCH (n:...) ... RETURN ...
      response: specific return type from conn.query() method
    all alternative names: # how it may be described
      - node
      - record
      - response
  identity:
    description: a unique ID of a node. I access it with queries to server. Can be requested as `ID(node)`.
    formats: # how it may be coded
      raw: remote data on server
      integer: like on server
      string: converted from integer
      part of response: specific return type from conn.query() method
    all alternative names: # how it may be described
      - identity
      - id
      - identities
      - id
      - df.Index (pd.DataFrame index)
      - field, column, key (part of `dict`, `pd.Series`, etc)
  variable name:
    description: whenever i send queries to db, i need to refer to nodes as `(variable_name:Label)...`. I can choose any, but the one in module `neo4j.Columns.NODE` is preferred.
    formats: # how it may be coded
      NODE: constant i defined earlier
      string: custom chosen one
    all alternative names: # how it may be described
      - variable name
      - node variable name
      - NODE
      - node
      - n
  row:
    description: an entry that i downloaded from some `db` and remembered into local variable (pd.DataFrame, dict, list, pd.Series, etc). Can be converted into `node`.
    formats: # how it may be coded
      dict: node fields = dict keys, node field values = dict values, node ID = in dict keys
      pd.Series: node fields = Series.index, node field values = Series values, node ID = in Series.index
      list: list of `dict`/`pd.Series`
      pd.DataFrame: node fields = DataFrame.columns, node field values = DataFrame values, node ID = DataFrame.index
    all alternative names: # how it may be described
      - row
      - data (some data)
      - entry
      - dictionary
      - definition (some data definition, for example `node definition`)
      - some contents (for example playlist contents)
      - settings (for the NodeViewer)
...